POLLINATORS POLLINATION AND FOOD PRODUCTION

THE ASSESSMENT REPORT ON POLLINATORS, POLLINATION AND FOOD PRODUCTION
data points taken over time may have an internal structure
(such as seasonal variation) that should be considered
(Montgomery et al., 2008). Thus, this approach is well suited
for valuing pollination services across temporal scales,
because several factors influencing pollination benefits can
be addressed and forecasted. This would include ecological
aspects, such as plant and pollinator phenological patterns
and future trends, pollinator abundance and diversity
changes, and economic variable, such as yield, production
costs and prices.
There are several different types of time series analyses
and models (see Tsay, 2002; Montgomery et al., 2008 for
a full compendium), but most studies regarding pollination
services usually adopt regression methods (Table 4.7).
More complex time series analyses, such as stochastic
simulations and complex forecasting models constitute a
powerful tool to determine the impacts of pollinator loss
under different land use scenarios (Keitt, 2009) but no
studies have yet applied these techniques to pollination
services (Section 7). Forecasting methods are frequently
used in econometrics, finance and meteorology, but their
use in ecological analyses is increasing (Clark et al., 2001).
Availability of new data sets and the development of
sophisticated computation and statistical methods, such
as hierarchical models (Clark et al., 2001), offer new venues
to work together with decision-makers to use forecasting
techniques in pollination service assessments.
3.2.3.2 Scenarios
A way of understanding the future is to create scenarios of
possible futures. The aim of scenarios is not to predict the
future evolution of our society but to discuss the impact of
pollinators under different possible futures of our society
(MEA, 2005). More precisely, a scenario is a storyline that
describes the evolution of the world from now to a possible
situation (Garry et al., 2003). Scenarios are constructed to
provide insight into drivers of change, reveal the implications
of current trajectories, and illuminate options for action. They
should compare at least two possible futures. Scenario
analysis typically takes two forms: quantitative modelling
(mathematical simulation models or dynamic program
models) and qualitative narrating (deliberative approaches
used to explore possible futures and describe how
society could be situated in these futures – MEA, 2005).
Qualitative deliberation can be undertaken between experts,
consultants, researchers and stakeholders.
2014) use this approach at the national scale. The SRES
scenarios project the future evolution of greenhouse gases
following the evolution of several driving forces, such as
demographic change, social and economic development,
and the rate and direction of technological change.
However, these scenarios do not take into account the
interaction between ecosystem services and our human
society. These issues were introduced by the MEA and
ALARM project.
The MEA defines four scenarios: Global Orchestration,
Order from Strength, Adapting Mosaic and Techno garden
(MEA, 2005). In the Techno garden and Adapting Mosaic
scenarios, ecosystem services are recognized as important
for society and need to be maintain and developed, whereas
in the Global Orchestration and Order from Strength
scenarios, they are replaced when it is possible or made
robust enough to be self-maintained. Pollination services
were explicitly addressed within these scenarios: Global
Orchestration, Order from Strength and Techno garden
projected a loss of pollination services because of species
losses, use of biocides, climate change, pollinator diseases
and landscape fragmentation. In the Adapting Mosaic
scenario, pollination services remain stable due to regional
ecosystem management programs.
However, these scenario options do not consider the
economic value of these changes. By contrast, Gallai et al.
(2009b) utilised existing estimates to project these values
in the ALARM scenarios. Three scenarios are defined by
the ALARM project (a Europe wide project on biodiversity):
BAMBU, GRAS and SEDG. BAMBU (Business As Might
Be Usual) refers to the expected continuation of the current
land use practices. The GRAS (GRowth Applied Strategy)
scenario is a kind of liberal scenario where the borders
between countries are considered open to free market
and the weight of restrictive policies is lower than BAMBU
scenario. The SEDG (Sustainable European Development
Goal) scenario focuses on the reduction of greenhouse
gases and, more generally, on climate change. Using the
land use change within each scenario, Gallai et al. (2009b)
evaluated the changes in the economic value of insect
pollinators to the Spanish and German agricultural sectors
in 2020. They demonstrated that the economic contribution
of insect pollinators would increase in Germany within GRAS
and BAMBU scenarios, while it would remain the same
within the SEDG scenario. On the other hand, the economic
value would decrease in all scenarios in Spain.
233
4. ECONOMIC VALUATION OF POLLINATOR GAINS
AND LOSSES
More recent scenarios often combine the qualitative and
quantitative approaches; e.g., the SRES scenarios (Special
Report: Emissions Scenarios; Nakicenovic et al. 2000), MEA
scenarios (MEA, 2005) or ALARM scenarios (Assessing
Large scale risks for biodiversity with tested methods;
Spangenberg et al. 2012, Settele et al. 2012) at the global
scale. Similarly, the UK NEA scenarios (Haines-Young et al.
The scenarios presented above are general (national or
global scales) and difficult to apply to a specific region.
Another study (Priess et al., 2007) used basic regression
models combined with metrics derived from field data to
analyse the impact of deforestation on pollination services
(in terms of revenue per hectare of coffee) in north-eastern
border of the Lore Lindu National Park (Indonesia). This